SSDenoDet: Self-Supervised Cross-Domain Feature Denoising for SAR Target Detection

Labeled synthetic aperture radar (SAR) detection data are costly, while speckle and small targets challenge conventional detectors. We present SSDenoDet, which transfers a corruption-robust masked-autoencoding representation learned from hyperspectral imagery (HSI) to SAR. A frozen MAESSI encoder and centered exponential-moving-average targets train Yield-Aware MAESSI-Aligned Learner (YAMAL), a grayscale SAR image encoder; an FPN then localizes targets from its spatial features. HSI and the teacher are absent at inference. SSDenoDet-E reaches 57.55 COCO mAP on untouched SARDet-100K validation versus 55.60 for our matched DenoDet V2 reproduction, and improves common-evaluator AP50 over its single-model counterpart on four independently trained SAR benchmarks.